Provenance: Tagion’s relation to DLTs

#1 in our series “Off the chain”

In this series of blog-posts we will explore the background for Tagion’s innovation in database technologies: how it compares to other decentralised architectures and how it best understood without recourse to blockchains.
In this first instalment, we start with Bitcoin and Ethereum, because many ideas in the public’s awareness of digital systems are still shape by them. In the following posts we will go deeper into issues of decentralisation that were mostly ignored by a public discourse blinded by the prospects of financial windfalls — but which make all the difference for business-applications, and what Tagion aims for.

When an unknown figure, masquerading behind the pseudonym Satoshi Nakamoto, published the Bitcoin Whitepaper in 2008, it set in motion financial and technological movements that, more than 15 years later, continue to shape the world.

Bitcoin was first “pitched” as an electronic cash system, a way to send money across the internet without banks, payment processors, or any financial middlemen inserting themselves into the equation. Though transformative, the vision of a supranational money and payment system predates Bitcoin itself. For decades, the idea had been kicked around by the Cypherpunk movement, a group of privacy-focused programmers convinced that cryptography could be a catalyst for social and political change. But early efforts, like DigiCash and B-Money, ran into the same problem: without a central authority keeping the books, what was to stop someone from duplicating cash and spending it twice?

Nakamoto’s breakthrough was to solve the so-called double-spending problem, introducing the first blockchain network where payments, conventionally enforced by centralised third-parties, were securely facilitated by mathematics and computational power in a decentralised environment.

This might not seem like a big deal, but it is. And to appreciate its significance, one needs only consider the complexity of executing cross-border payments using legacy infrastructure. Funds must pass through a maze of intermediaries, such as correspondent banks, clearinghouses, and payment processors, whose role is to transmit information, verify transactions and reconcile records, with every intermediary involved adding a layer of cost and delay, making cross-border payments anything but seamless.

Against this backdrop, Bitcoin’s peer-to-peer payment service presents itself as an appealing alternative, albeit with some notable limitations. To name just one, it remains largely confined to transferring its native currency — self-referentially named Bitcoin — from one wallet to another. And while Bitcoin (the currency) has managed to carve out a reputation as digital gold, attracting speculators and digital prospectors alike, the network’s utility therefore remains rather limited, with few practical applications beyond sending and hoarding this so-called cryptocurrency.

The Rise of Programmable Blockchains

While Bitcoin’s use cases are narrow, the same can’t be said for the technology powering it, namely blockchain. More than just a tool for shuffling “digital gold” around, blockchain offers a secure and transparent way to record and verify data, with applications spanning finance, supply chains, governance, and beyond.

One of the first to recognise this potential was the Canadian-Russian programmer Vitalik Buterin. In early 2014, he officially published the Ethereum Whitepaper, outlining his vision for a decentralised platform capable of supporting advanced applications. By designing Ethereum as a blockchain with built-in, Turing-complete programming languages, he and fellow Ethereans enabled users to create complex, self-executing functions — so-called smart contracts — expanding blockchain’s capabilities far beyond simple peer-to-peer transactions.

For the uninitiated, the novelty and practical implications of Vitalik’s vision may be lost. But one way to make it tangible is by looking at how it transforms asset settlement, a process conventionally known as Delivery-versus-Payment, or DvP, where the transfer of securities and payment must occur simultaneously to mitigate counterparty risk, that is, the risk that one party fails to deliver on their side of the deal.

On Ethereum, both fiat-pegged cash, known as stablecoins, and tokenised securities like bonds or stocks co-exist on the same decentralised infrastructure. Not only can these assets be transferred seamlessly between wallets in a peer-to-peer manner, but they can also be traded directly against each other using smart contracts, resulting in shorter settlement times and, when network conditions permit, negligible fees. Here, payment is released only if the asset is successfully transferred, and vice versa, a mechanism known in crypto-speak as an atomic swap. This means that either both sides of the trade go through, or neither does, effectively reducing counterparty risk to zero.

Contrast this with how DvP works in traditional finance, where settling an asset trade is a multi-layered process involving numerous intermediaries, including central securities depositories (CSD), brokers, dealers, custodians, clearinghouses (CCP), stock exchanges, and banks. Each entity plays a role in ensuring the trade is executed securely, but their involvement introduces inefficiencies, adding delays and increasing costs.

What makes Ethereum’s approach so compelling is its ability to replace these layers of intermediaries with automated smart contracts. These contracts enforce the terms of the trade directly on the blockchain, ensuring that payment is released only when the asset is successfully transferred. Said differently, smart contracts follow a simple “if → then” logic: if one condition is met, another action follows, eliminating the need for third-party enforcement.

While this is particularly useful in asset settlement, its usefulness extends far beyond, streamlining workflows across industries and use cases by eliminating intermediaries and reducing the need for manual oversight. Paired with its data immutability — bringing transparency and trust to multi-stakeholder environments — it’s no surprise that blockchain is increasingly used as critical infrastructure in finance and supply chains.

Now, one might be tempted to treat the “if → then” logic of smart contracts as the big breakthrough. It isn’t. Conditional execution has been around for decades in databases and rule engines. What changed with Ethereum, and blockchain in general, is the substrate those conditions run on: a tamper-resistant, append-only ledger replicated across many nodes. Meaning everyone sees the same state, everyone sees the same history, everyone can verify the same rules, and no single party can quietly rewrite them to their advantage.

By bringing conditional execution to blockchain, Ethereum has thus created a general-purpose platform perfectly suited for multi-stakeholder environments, in which people or organisations need to coordinate on shared data but cannot, or prefer not to, place full trust in a single controlling party. In such settings, the blockchain itself becomes the impartial record-keeper, and the code deployed on it becomes the impartial rule-enforcer. And this, of course, is incredibly powerful in any environment where there’s no guarantee that the involved parties can necessarily trust each other, whether because of competing incentives, the risk of operational errors, or outright malicious intent.

No Horses for Courses

But true to the adage, ‘horses for courses’, efficiency comes from aligning the right tool with the right challenge. And while Ethereum is highly versatile, it may not always be the ideal choice for companies. Some may require a more specialised infrastructure that better supports their specific needs, especially pertaining to throughput, costs, data handling, governance, and privacy. This has led to the rise of Ethereum alternatives, or, as some sinisterly refer to them, Ethereum killers. While many of these is built using the blockchain technology, others have ventured beyond, exploring different approaches like Hashgraph, Tempo, and Notary Services. The growing variety of competing technologies has led to the broader concept of Distributed Ledger Technology (DLT), an umbrella term that encompasses not only blockchains, but any system designed to maintain a shared, tamper-resistant ledger of data across multiple participants without relying on a central authority.

These networks, in their bid to outpace Ethereum, pursue scalability through faster consensus algorithms and, as is often the case, by limiting the number of nodes required for consensus, while increasing the performance demands on each participant (we cover what this means in our second article in the series). The result is systems that are faster, yes, but also more centralised and arguably less resilient. Performance, then, is improved, but only by compromising on decentralisation.

Performance isn’t the only axis along which structural trade-offs appear. Just as many networks achieve scalability by compromising on decentralisation, most decentralised networks enforce immutability at the expense of data flexibility, meaning that once data is written, it cannot be modified or removed. While this underpins data integrity and auditability, it makes it difficult to support error correction, data deletion, or compliance with evolving data regulations.

Privacy, too, presents a trade-off. In many decentralised networks, particularly public, permissionless ones, transparency is a guiding principle. Transactions, counterparties, amounts, and even network-wide behavioural patterns are visible to anyone. This, on the one hand, supports auditability, but on the other, it limits confidentiality. And for sectors like finance, healthcare, or supply chain, where discretion is essential, such exposure introduces real challenges.

So even as the networks diversify in form, many of these so-called Ethereum alternatives remain bound by the same core constraints: They offer a high degree of scalability, immutability, and audibility, but only by sacrificing decentralisation, flexible data handling, and privacy. Alas, these are features that users increasingly demand. And so, even with an expanding number of networks, with an expanding number of features, only a handful, if any, are equipped to meet the versatile demands of today’s users. To return to the earlier adage: many courses, then, are left without horses.

In what follows, we take a closer look at these limitations, beginning with the scaling challenges faced by decentralised networks. From there we will explore the systemic trade-offs that tie down any chances for widespread adoption of decentralised systems in business and consumer applications — the chains that Tagion is set to break!